Zhen Li

Yanyan Wang, Yingying Wang, Junli Liang et al.

The rapid advancement of large language models (LLMs) has significantly propelled progress in artificial intelligence, demonstrating substantial application potential across multiple specialized domains. Telecommunications operation scheduling (OS) is a critical aspect of the telecommunications industry, involving the coordinated management of networks, services, risks, and human resources to optimize production scheduling and ensure unified service control. However, the inherent complexity and domain-specific nature of OS tasks, coupled with the absence of comprehensive evaluation benchmarks, have hindered thorough exploration of LLMs' application potential in this critical field. To address this research gap, we propose the first Telecommunications Operation Scheduling Evaluation Benchmark (TeleEval-OS). Specifically, this benchmark comprises 15 datasets across 13 subtasks, comprehensively simulating four key operational stages: intelligent ticket creation, intelligent ticket handling, intelligent ticket closure, and intelligent evaluation. To systematically assess the performance of LLMs on tasks of varying complexity, we categorize their capabilities in telecommunications operation scheduling into four hierarchical levels, arranged in ascending order of difficulty: basic NLP, knowledge Q&A, report generation, and report analysis. On TeleEval-OS, we leverage zero-shot and few-shot evaluation methods to comprehensively assess 10 open-source LLMs (e.g., DeepSeek-V3) and 4 closed-source LLMs (e.g., GPT-4o) across diverse scenarios. Experimental results demonstrate that open-source LLMs can outperform closed-source LLMs in specific scenarios, highlighting their significant potential and value in the field of telecommunications operation scheduling.

Zhengda Li

Knowledge distillation (KD) is a widely used technique to transfer knowledge from a large teacher network to a smaller student model. Traditional KD uses a fixed balancing factor alpha as a hyperparameter to combine the hard-label cross-entropy loss with the soft-label distillation loss. However, a static alpha is suboptimal because the optimal trade-off between hard and soft supervision can vary during training. In this work, we propose an Adaptive Knowledge Distillation (AKD) framework. First we try to make alpha as learnable parameter that can be automatically learned and optimized during training. Then we introduce a formula to reflect the gap between the student and the teacher to compute alpha dynamically, guided by student-teacher discrepancies, and further introduce a Context-Aware Module (CAM) using MLP + Attention to adaptively reweight class-wise teacher outputs. Experiments on CIFAR-10 with ResNet-50 as teacher and ResNet-18 as student demonstrate that our approach achieves superior accuracy compared to fixed-weight KD baselines, and yields more stable convergence.